Optimization method for mapping virtual machines to physical machines

Abstract

The invention discloses an optimization method for mapping virtual machines to physical machines. The method comprises the following steps of S1, modeling virtual machine demands; S2, determining a threshold; S3, establishing a priority queue; S4, carrying out batch dequeuing; S5, designing an optimization kernel; and S6, carrying out algorithm parallelization. According to the optimization methodfor mapping the virtual machines to the physical machines provided by the invention, the validity and low delay property of the algorithm are improved. According to the method, allocation positions of the virtual machines can be timely and effectively determined according to the specific virtual machine demands; and when the virtual demands are dynamically changed, the targeted dynamic adjustmentis carried out for the mapping from virtual machine resources to physical machine resources. In an application process, a utilization rate of cloud resources can be improved, and the operation cost of a cloud service provider is reduced.

Description

technical field [0001] The invention relates to a cloud resource scheduling algorithm, in particular to a cloud resource scheduling algorithm optimization method for mapping a virtual machine to a physical machine. Background technique [0002] At present, cloud resources are characterized by heterogeneity, uncertainty, and dynamics, and are sensitive to delay requirements in the scheduling process. These unique properties of cloud resources increase the difficulty of cloud resource scheduling algorithm design. [0003] In order to reduce the processing delay of cloud resource scheduling and improve the efficiency of cloud resource scheduling, evolutionary computing is currently used to schedule cloud resources, mainly using randomized data as input, and solving suboptimal results through a sufficient number of iterations . Since the acceptable suboptimal solution is used to approach the optimal solution, the time cost of problem solving is reduced, and it is suitable for ...

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Problems solved by technology

[0004] At present, the scheduling algorithms mainly used in cloud resource scheduling technology are divided into three categories: one is the integer programming algorithm, which can obtain the optimal mapping of cloud resources when the optimal solution exists, but its defect lies in the calculation and analysis delay. The second is supervised machine learning algorithms, which require a large amount of historical data as the support of the scheduling algorithm, and require a large number of iterations to approach the optimal solution. The obvious dynamic and uncertain characteristics of cloud

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